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In recent years, the development of hydrogen energy has been widely discussed, particularly in combination with renewable energy sources, enabling the production of "green" hydrogen. With the significant increase in wind power generation, a promising solution for obtaining green hydrogen is the development of wind-to-hydrogen (W2H) systems. However, the high proportion of wind power and electrolyzers in a large-scale W2H system will bring about the problem of renewable energy consumption and frequency stability reduction. This paper analyzes the operational characteristics and economic feasibility of mainstream electrolyzers, leading to the proposal of a coordinated hydrogen production scheme involving both a proton exchange membrane (PEM) electrolyzer and an alkaline (ALK) electrolyzer. Subsequently, a coordinated control based on Model Predictive Control (MPC) is proposed for system frequency regulation in a large-scale W2H islanded microgrid. Finally, simulation results demonstrate that the system under PEM/ALK electrolyzers coordinated control not only flexibly accommodates fluctuating wind power but also maintains frequency stability in the face of large disturbances. Compared with the traditional system with all ALK electrolyzers, the frequency deviation of this system is reduced by 25%, the regulation time is shortened by 80%, and the demand for an energy storage system (ESS) is reduced. The result validates the effectiveness of MPC and the benefits of the PEM/ALK electrolyzers coordinated hydrogen production scheme. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) has become a promising technique to facilitate distributed energy resources (DERs) to participate in the power markets. Considering the respective interests of the VPP agent and distribution system operator (DSO), a bi-level optimisation model for VPP bidding in multiple retail markets (including active power, reactive power and spinning reserve market) as price-maker is formulated. In the upper layer, taking into account various DERs, the VPP agent aims to develop hourly bidding prices and quantities of multiple market commodities to maximise its operation profits. In the lower layer, DSO conducts the retail market clearing to minimise the system operation cost considering the network constraints and bidding plans of the market participants. Moreover, the quadratic coupling constraints among different market commodities due to capacity limitation of distributed generators and branches are formulated explicitly in the proposed model. The hybrid simulated annealing-genetic algorithm with self-adaptive parameters is adopted to cope with the non-linearity and compute the economic bidding plans for VPP. The effectiveness of the proposed approach is verified under different scenarios through case studies, which indicate its superiority and great potential for implementation. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) has become a promising technique to facilitate distributed energy resources (DERs) to participate in the power markets. Considering the respective interests of the VPP agent and distribution system operator (DSO), a bi‐level optimisation model for VPP bidding in multiple retail markets (including active power, reactive power and spinning reserve market) as price‐maker is formulated. In the upper layer, taking into account various DERs, the VPP agent aims to develop hourly bidding prices and quantities of multiple market commodities to maximise its operation profits. In the lower layer, DSO conducts the retail market clearing to minimise the system operation cost considering the network constraints and bidding plans of the market participants. Moreover, the quadratic coupling constraints among different market commodities due to capacity limitation of distributed generators and branches are formulated explicitly in the proposed model. The hybrid simulated annealing‐genetic algorithm with self‐adaptive parameters is adopted to cope with the non‐linearity and compute the economic bidding plans for VPP. The effectiveness of the proposed approach is verified under different scenarios through case studies, which indicate its superiority and great potential for implementation. [ABSTRACT FROM AUTHOR]

The environmental/economic dispatch (EED) of power systems addresses the environmental pollution problems caused by power generation at the operational level, offering macroscopic control without requiring additional construction and remediation costs, garnering widespread attention in recent years. This paper undertakes a comprehensive review of existing EED models, categorizing them according to the control of atmospheric pollutants into total air pollutant control (TAPC) and control considering the spatial and temporal diffusion (STD) of atmospheric pollutants. In addition, various methods employed to address the EED problems, as well as the current state of research on multi-area EED models, are presented. Finally, this paper analyzes and summarizes the literature on existing EED models, highlighting the deficiencies of the current work and future research directions. Through these explorations, the authors find that controlling the EED model by considering TAPC is more suitable for general macro planning, whereas the EED model considering the STD of air pollutant emissions enables more precise and effective control. Summarizing such models and techniques is conducive to developing dispatch plans adapted to local conditions, which is significantly beneficial for public welfare and government management, promoting sustainable and environmentally friendly power system dispatch methods. [ABSTRACT FROM AUTHOR]

Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS. [ABSTRACT FROM AUTHOR]

To maintain the system's real-time balance, mature electricity markets generally tend to raise the frequency of market clearing. Given the shrinking time lag between the price update process and underlying electromechanical dynamics, it inevitably considers the two in a unified, dynamical framework. However, the relationship between market activities and the evolution of electricity networks has received scant attention in pertinent research. Inspired by the primal-dual gradient method and modern control theory, we propose a novel multi-rate hybrid model that simultaneously accounts for the continuous nature of physical system dynamics and the discrete nature of market clearing. To maintain the asymptotical convergence of the proposed model, we derive the explicit constraints on the upper bounds of market clearing and bidding intervals. These bounds can be determined without prior knowledge of the Nash equilibrium, and even the time schedules don't have to be periodic, which dramatically improves the applicability of the proposed model in practical market operation. The whole model provides a new perspective to study economic-physical problems, which can be readily implemented in a distributed way and achieve social welfare maximization along with frequency regulation. • A novel economic-physical model is established for the market operation coupled with the power networks' dynamics. • The proposed hybrid model considers frequency evolution's continuous nature and bidding's discrete nature. • We derive the upper bounds on the time lengths of consecutive bidding and market clearing. • The multi-rate hybrid system can maximize social welfare and regulate frequency. [ABSTRACT FROM AUTHOR]

Intelligent home energy management system (IHEMS) manages various home appliances depending on user preferences in order to save energy cost and assure user satisfaction. To realize cooperation between the distribution system operator (DSO) and end-user, a two-stage scheduling optimization model is developed based on the flexibility quantification, with the user privacy in demand-side management in a specific cluster taken into account. It also aids to reduce the network operation cost, and the distribution system's uncertainty caused by wind power is represented using chance constraints. The user uploads the IHEMS-calculated offered flexibility to DSO via cluster; DSO optimizes the optimal flexibility to minimize the operation cost; then, clusters distribute the flexibility requirement based on the flexibility index; finally, the user updates the optimal schedule. The numerical results using MATLAB are provided to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

• Provide a new perspective to study the market operation based on control theory. • Establish a dynamic economic-physical model for the market operation. • The game action is described by state equations derived from the primal-dual method. • The whole trading process can be executed in a distributed way. • The stability analysis relies on the Lyapunov function and incremental passivity. With the emergency of competitive markets as central operational mechanisms, the real-time market clearing results are used to determine the optimal power dispatch. It inevitably strengthens the coupling between the market update process and the physical response of the generators and networks. It necessitates the development of stability analysis for such coupled systems. Based on the primal–dual gradient method, an economic cybernetic model is established to simultaneously characterize the market operation and electromechanical power system dynamics. Inspired by modern control theory, the proposed dynamic model constructs a state space in which the actions of market participants are control signals, and the system states, such as nodal prices, bus voltages, and frequency, are treated as state variables. To promote the expandability, we also formulate the coupled dynamics in port-Hamiltonian form and provide detailed proof and sufficient conditions for its asymptotic stability by using properties of incremental passive systems. The whole transaction process implemented in a distributed manner aims at maximizing social welfare and frequency regulation when the economic-physical system reaches its equilibrium point. [ABSTRACT FROM AUTHOR]